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Something was bothering me for almost two decades. It was a pen and paper game that I learned when I was around 13. The rules are simple: on an empty 10x10 grid (100 squares in total) you put a number 1 on an arbitrary square. Starting from that square you can move horizontally or vertically jumping over two squares or diagonally jumping over one square. There you can place number 2. Your task is to reach number 100, filling all squares. You can not visit already visited squares. Here is an example of a solved game with a reduced 5x5 grid, starting at top-left corner:

12414225162158201310182311471536172212919
On the other hand, if the program makes bad choices, we might get stuck without reaching the perfect score of 25 (on a reduced 5x5 grid):

18291613517141047153619121811
Notice how we got stuck at number 19, unable to move anywhere and fill six remaining gaps. On an original 10x10 grid I never managed to reach the perfect score of 100. Countless hours wasted at school, of trial and …

Summary (reading time: 10 minutes)Deadlocks are caused by many threads locking the same resourcesDeadlocks can also occur if thread pool is used inside a task running in that poolModern libraries like RxJava/Reactor are also susceptible
A deadlock is a situation where two or more threads are waiting for resources acquired by each other. For example thread A waits for lock1 locked by thread B, whereas thread B waits for lock2, locked by thread A. In worst case scenario, the application freezes for an indefinite amount of time. Let me show you a concrete example. Imagine there is a Lumberjack class that holds references to two accessory locks:

Almost a year ago Spring team announced spring-cloud-function umbrella project. It's basically a Spring's approach to serverless (I prefer the term function-as-a-service) programming. Function<T, R> becomes the smallest building block in a Spring application. Functions defined as Spring beans are automatically exposed e.g. via HTTP in RPC style. Just a quick example how it looks:

data:{"1F60D":1}
data:{"1F3A8":1,"1F48B":1,"1F499":1,"1F602":1,"2764":1}
data:{"1F607":1,"2764":2}
Each message represents the number of various emojis that appeared on Twitter since the previous message. After a few transformations, we got a stream of hexadecimal Unicode values for each emoji. E.g. for {"1F607":1,"2764":2} we produce three events: "1F607", "2764", "2764". This is how we achieved it:

In this article we will learn how to consume infinite SSE (server-sent events) stream with Spring's WebClient and Project Reactor. WebClient is a new HTTP client in Spring 5, entirely asynchronous and natively supporting Flux and Mono types. You can technically open thousands of concurrent HTTP connections with just a handful of threads. In standard RestTemplate one HTTP connection always needs at least one thread.

As an example, let's connect to this cute little site called emojitracker.com. It shows emojis being used in real-time on Twitter. Looks quite cool! All credits go to Matthew Rothenberg, the creator of that site. It's very dynamic so there obviously has to be some push mechanism underneath. I wore my hacker glasses and after hours of penetration testing, I discovered the following URL in Chrome DevTools: http://emojitrack-gostreamer.herokuapp.com/subscribe/eps. If you connect to it, you'll get a fast stream of emoji counters:

we are mixing business and technical dependencies in our componentstherefore I am sometimes reluctant to add new metrics because it requires me to inject MetricRegistryalso MetricRegistry must be stubbed in unit tests
Micrometer's tagline is:

Think SLF4J, but for metrics

It's actually quite accurate. Whenever I need a Logger I don't inject LoggerFactory, instead I simply use static methods available everywhere. The same goes for Micrometer, I simply use static factory methods on globally available Metrics class:

In the previous article we created a simple indexing code that hammers ElasticSearch with thousands of concurrent requests. The only way to monitor the performance of our system was an old-school logging statement:

.window(Duration.ofSeconds(1))
.flatMap(Flux::count)
.subscribe(winSize -> log.debug("Got {} responses in last second", winSize));
It's fine, but on a production system, we'd rather have some centralized monitoring and charting solution for gathering various metrics. This becomes especially important once you have hundreds of different applications in thousands of instances. Having a single graphical dashboard, aggregating all important information, becomes crucial. We need two components in order to collect some metrics:

publishing metricscollecting and visualizing them
Publishing metrics using Dropwizard Metrics
In Spring Boot 2 Dropwizard Metrics were replaced by Micrometer. This article uses the former, the next one will show the latter solution in pr…